PhD Oral Exam - Pengcheng Li, Mechanical Engineering

When studying for a doctoral degree (PhD), candidates submit a thesis that provides a critical review of the current state of knowledge of the thesis subject as well as the student’s own contributions to the subject. The distinguishing criterion of doctoral graduate research is a significant and original contribution to knowledge.

Once accepted, the candidate presents the thesis orally. This oral exam is open to the public.

Abstract

Parallel robots, so-called parallel kinematic machines or parallel manipulators, are closed loop chain mechanisms whose end-effectors are actuated by a series of independent computer-controlled serial chains linked to the base. Parallel robots present some outstanding advantages in high force- to-weight ratio, better stiffness and theoretical higher accuracy compared with serial manipulators. Hence parallel robots have been utilized increasingly in various applications. However, due to the manufacturing tolerances and defections in the robot structure, the positioning accuracy of parallel robots is basically equivalent with that of serial manipulators according to previous researches on the accuracy analysis of the Stewart Platform [1], which is difficult to meet the precision requirement of many potential applications. In addition, the existence of closed-chain mechanism yields difficulties in designing control system for practical applications, due to its highly coupled dynamics. Especially the low accuracy of the robot is the main problem for the off-line programming based applications where tens of thousands points or trajectories are to be reached (e.g., pick-and-place [2] and machine tools [3]).

Kinematic calibration is an effective method to remove the negative influence of these errors and to improve the positioning accuracy of end-effector in a robot control system. Moreover, the dynamic model plays an important role in the model-based controller design. To perform the kinematic calibration, redundant data from various types of sensors, e.g., the position of the end-effector, is commonly needed to solve the optimization problem [4]. Visual sensor is a good choice for pro- viding non-contact measurement of the end-effector pose (position and orientation) with simplicity in operation and low cost compared to other measurement methods such as the coordinate measurement machine (CMM) [4] and the laser tracker [5]. Therefore, the visual sensor has a great potential to be incorporated in the kinematic calibration, dynamic identification and controller design of the parallel robot. In this research, a series of solutions including kinematic calibration, dynamic identification and visual servoing are proposed to improve the positioning and tracking performance of the parallel robot based on the visual sensor.

The main contributions of this research include three parts. In the first part, a relative posture- based algorithm (RPBA) is proposed to solve the kinematic calibration problem of a six-revolute- spherical-spherical (6-RSS) parallel robot by using the optical CMM sensor. The relative posture of robot is estimated by using the detected pose w.r.t. the sensor frame through several reflectors which are fixed on the robot end-effector. Based on the relative pose, a calibration algorithm is proposed to determine the optimal error parameters of the robot kinematic model and external parameters introduced by the optical sensor. The simulation results validate the superiority of the algorithm by comparing with the classic implicit calibration method. And the experimental results demonstrate that the proposal RPBA using optical CMM is an implementable and effective method for the parallel robot calibration.

The second part focuses on the dynamic model identification of the 6-RSS parallel robots. A visual closed-loop output-error identification method based on an optical CMM sensor is proposed for the purpose of the advanced model-based visual servoing control design of parallel robots. The dynamic model of the parallel robot is derived based on the virtual work principle, and the built dynamic model is verified through Matlab/SimMechanics. By using an outer loop visual servoing controller to stabilize both the parallel robot and the simulated model, the visual closed-loop output-error identification method is developed and the model parameters are identified by using a nonlinear optimization technique. The effectiveness of the proposed identification algorithm is validated by experimental tests.

In the last part, a dynamic sliding mode control (DSMC) scheme combined with the visual servoing method is proposed to improve the tracking performance of the 6-RSS parallel robot based on the optical CMM sensor. By employing a position-to-torque converter, the torque command generated by DSMC can be applied to the position controlled industrial robot. The stability of the proposed DSMC has been proved by using Lyapunov theorem. The real-time experiment tests on a 6-RSS parallel robot demonstrate that the developed DSMC scheme is robust to the modeling errors and uncertainties. Compared with the classical kinematic level controllers, the proposed DSMC exhibits the superiority in terms of tracking performance and robustness.